We train probabilistic kernel support vector machines (SVMs) [1]}, but any non-linear model suffices.
For each setting of the simulation (i.e., unique combination of simulation parameters), we train two SVMs with each model using one set of labels \(y, \tilde{y}\)  on the same 100 realizations of the simulation with 2,000 training data points and evaluate all models on a simulated sample of 20,000 test samples. As preprocessing, we apply one-hot encoding to \(\mathbf {x}\)  after discretization.
